Applied Machine Learning Explainability Techniques

Chapter 8: Human-Friendly Explanations with TCAV

In the previous few chapters, we have extensively discussed LIME and SHAP. You have also seen the practical aspect of applying the Python frameworks of LIME and SHAP to explain black-box models. One major limitation of both frameworks is that the method of explanation is not extremely consistent and intuitive with how non-technical end users would explain an observation. For example, if you have an image of a glass filled with Coke and use LIME and SHAP to explain a black-box model used to correctly classify the image as Coke, both LIME and SHAP would highlight regions of the image that lead to the correct prediction by the trained model. But if you ask a non-technical user to describe the image, ...